Discovery of the reward function for embodied reinforcement learning agents

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Neuroscience

Background:

• Reward maximization is crucial for survival, evolution, and cognitive abilities like learning in biological organisms and embodied agents.
• Reinforcement learning (RL) shows promise for intelligent decision-making in embodied agents but faces challenges with complex, uncertain real-world tasks.
• Current reward function design for embodied RL agents requires manual engineering, domain expertise, and extensive tuning, leading to inefficiency and potential failure.

Purpose of the Study:

• To introduce a bilevel optimization framework for discovering optimal reward functions for embodied reinforcement learning agents.
• To address the limitations of conventional, manually engineered reward signals in complex and uncertain environments.
• To accelerate policy optimization and improve adaptability of embodied RL agents across diverse tasks.

Main Methods:

• Developed a bilevel optimization framework.
• Utilized regret minimization to discover optimal reward functions.
• Applied the framework to embodied reinforcement learning agents.

Main Results:

• The proposed framework accelerates policy optimization for embodied RL agents.
• The approach enhances the adaptability of RL agents across a variety of tasks.
• Demonstrated an effective mechanism for discovering optimal reward functions, overcoming manual engineering limitations.

Conclusions:

• The bilevel optimization framework offers a promising solution for designing effective reward functions in embodied RL.
• This method can significantly improve the efficiency and adaptability of embodied agents in complex real-world scenarios.
• Findings support the wider adoption of embodied RL in various scientific disciplines and advance the pursuit of artificial general intelligence.